MLG 023 Deep NLP 2

Aug 20, 2017

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INSIGHT

RNNs Key for Sequence Data

Recurrent Neural Networks (RNNs) are the premier deep learning architecture for sequence data like natural language.
Unlike feedforward networks, RNNs loop back to previous steps, allowing them to handle sequences through time.

INSIGHT

Supervised learning suits NLP where labeled sequences guide prediction of next words.
Reinforcement learning learns optimal actions to maximize rewards and fits better for tasks like video game AI, not NLP.

ANECDOTE

For stock market prediction, an RNN can do regression to predict numerical next values.
You must build a trading strategy using these predictions; the model doesn't automatically learn buy/sell actions.

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Notes and resources at ocdevel.com/mlg/23

Neural Network Types in NLP

Vanilla Neural Networks (Feedforward Networks):
- Used for general classification or regression tasks.
- Examples include predicting housing costs or classifying images as cat, dog, or tree.
Convolutional Neural Networks (CNNs):
- Primarily used for image-related tasks.
Recurrent Neural Networks (RNNs):
- Used for sequence-based tasks such as weather predictions, stock market predictions, and natural language processing.
- Differ from feedforward networks as they loop back onto previous steps to handle sequences over time.

Key Concepts and Applications

Supervised vs Reinforcement Learning:
- Supervised learning involves training models using labeled data to learn patterns and create labels autonomously.
- Reinforcement learning focuses on learning actions to maximize a reward function over time, suitable for tasks like gaming AI but less so for tasks like NLP.
Encoder-Decoder Models:
- These models process entire input sequences before producing output, crucial for tasks like machine translation, where full context is needed before output generation.
- Transforms sequences to a vector space (encoding) and reconstructs it to another sequence (decoding).
Gradient Problems & Solutions:
- Vanishing and Exploding Gradient Problems occur during training due to backpropagation over time steps, causing information loss or overflow, notably in longer sequences.
- Long Short-Term Memory (LSTM) Cells solve these by allowing RNNs to retain important information over longer time sequences, effectively mitigating gradient issues.

LSTM Functionality

An LSTM cell replaces traditional neurons in an RNN with complex machinery that regulates information flow.
Components within an LSTM cell:
- Forget Gate: Decides which information to discard from the cell state.
- Input Gate: Determines which information to update.
- Output Gate: Controls the output from the cell.